def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_data_src (List[List[str]]): List of sentences (words) in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
example_hyps = model.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def beam_search(model: NMT, test_iterator: BucketIterator, beam_size: int,
max_decoding_time_step: int) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_iterator BucketIterator: BucketIterator in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
# for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
for i, batch in enumerate(test_iterator):
src_sents, src_sents_lens = batch.src
src_sents = src_sents.permute(1, 0)
for j in range(len(src_sents_lens)):
src_sent = src_sents[j]
example_hyps = model.beam_search(
src_sent,
src_sents_lens[j],
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
def beam_search2(model1: NMT, model2: DPPNMT, test_data_src: List[List[str]],
beam_size: int, max_decoding_time_step: int,
test_data_tgt) -> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
@param model (NMT): NMT Model
@param test_data_src (List[List[str]]): List of sentences (words) in source language, from test set.
@param beam_size (int): beam_size (# of hypotheses to hold for a translation at every step)
@param max_decoding_time_step (int): maximum sentence length that Beam search can produce
@returns hypotheses (List[List[Hypothesis]]): List of Hypothesis translations for every source sentence.
"""
model1.eval()
model2.eval()
i = 0
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
hyp1 = model1.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hyp2 = model2.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
ref = test_data_tgt[i][1:-1]
#print(ref, hyp1[0].value)
bleu_topk = sentence_bleu(ref, hyp1[0].value)
bleu_dpp = sentence_bleu(test_data_tgt[i], hyp2[0].value)
#print(bleu_topk, bleu_dpp)
if bleu_dpp > bleu_topk:
print(i)
print(" ".join(hyp1[0].value))
print(" ".join(hyp2[0].value))
print(" ".join(ref))
i += 1
def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: int, max_decoding_time_step: int)\
-> List[List[Hypothesis]]:
""" Run beam search to construct hypotheses for a list of src-language sentences.
:param NMT model: NMT Model
:param List[List[str]] test_data_src: List of sentences (words) in source language, from test set
:param int beam_size: beam_size (number of hypotheses to keep for a translation at every step)
:param int max_decoding_time_step: maximum sentence length that beam search can produce
:returns List[List[Hypothesis]] hypotheses: List of Hypothesis translations for every source sentence
"""
was_training = model.training
model.eval()
hypotheses = []
with torch.no_grad():
for src_sent in tqdm(test_data_src, desc='Decoding', file=sys.stdout):
example_hyps = model.beam_search(
src_sent,
beam_size=beam_size,
max_decoding_time_step=max_decoding_time_step)
hypotheses.append(example_hyps)
if was_training: model.train(was_training)
return hypotheses
